Abstract
Information work involves the frequent (re)use of information objects (e.g. files, web sites, emails) for different tasks. Information reuse is complicated by the scattered organization of information among different locations. Therefore, access support based on recommendations is beneficial. Still, support needs to consider the ad-hoc nature of information work and the resulting uncertainty of information requirements. We present PASTREM, an ontology-based recommender system which proactively proposes information objects for reuse while a user interacts with a computer. PASTREM reflects the ad-hoc nature of information work and allows users to switch seamlessly between recommendations for more multitasking oriented or more focused work. This chapter describes the PASTREM recommender, the used data foundation of interaction histories, data storage in an ontology and the process of recommendation elicitation. PASTREM is evaluated in comparison with other, activity related recommendation approaches for information reuse, namely last recently used, most often used, longest used and semantically related. We report on strength and weaknesses of the approaches and show the benefits of PASTREM as recommender which considers the difference between single task focused and multitasking oriented recommendations.
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Notes
- 1.
PASTREM refers to the supported process: the REMembering of useful information objects which already have been used in the PAST.
- 2.
An example is a priest who contracts a marriage.
- 3.
Dyonipos uses ontologies only to capture events in interaction histories. The classifiers do not extend the ontology.
- 4.
- 5.
From now on and throughout the paper entities that belong to CWO are given without prefix. For all other entities, the respective prefix is given.
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Schmidt, B., Godehardt, E., Paulheim, H. (2013). PASTREM: Proactive Ontology Based Recommendations for Information Workers. In: Hussein, T., Paulheim, H., Lukosch, S., Ziegler, J., Calvary, G. (eds) Semantic Models for Adaptive Interactive Systems. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-5301-6_6
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